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Cooperative heterogeneous computing for parallel processing on CPU/GPU hybrids

机译:用于CPU / GPU混合体并行处理的协作异构计算

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This paper presents a cooperative heterogeneous computing framework which enables the efficient utilization of available computing resources of host CPU cores for CUDA kernels, which are designed to run only on GPU. The proposed system exploits at runtime the coarse-grain thread-level parallelism across CPU and GPU, without any source recompilation. To this end, three features including a work distribution module, a transparent memory space, and a global scheduling queue are described in this paper. With a completely automatic runtime workload distribution, the proposed framework achieves speedups as high as 3.08 compared to the baseline GPU-only processing.
机译:本文提出了一种协作式异构计算框架,该框架能够有效利用CUDA内核的主机CPU内核的可用计算资源,而CUDA内核只能在GPU上运行。提议的系统在运行时利用了CPU和GPU上的粗粒度线程级并行性,而无需重新编译任何源代码。为此,本文描述了三个功能,包括工作分配模块,透明的存储空间和全局调度队列。与仅使用GPU的基准处理相比,通过完全自动化的运行时工作负载分配,所提出的框架可实现高达3.08的加速。

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